# %%
"""
<table class="ee-notebook-buttons" align="left">
    <td><a target="_blank"  href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/extract_value_to_points.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
    <td><a target="_blank"  href="https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Image/extract_value_to_points.ipynb"><img width=26px src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png" />Notebook Viewer</a></td>
    <td><a target="_blank"  href="https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Image/extract_value_to_points.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a></td>
</table>
"""

# %%
"""
## Install Earth Engine API and geemap
Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://geemap.org). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.
The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet.
"""

# %%
# Installs geemap package
import subprocess

try:
    import geemap
except ImportError:
    print('Installing geemap ...')
    subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])

# %%
import ee
import geemap

# %%
"""
## Create an interactive map 
The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. 
"""

# %%
Map = geemap.Map(center=[40,-100], zoom=4)
Map

# %%
"""
## Add Earth Engine Python script 
"""

# %%
# Add Earth Engine dataset
# Input imagery is a cloud-free Landsat 8 composite.
l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1')

image = ee.Algorithms.Landsat.simpleComposite(**{
    'collection': l8.filterDate('2018-01-01', '2018-12-31'),
    'asFloat': True
})

# Use these bands for prediction.
bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11']

# Load training points. The numeric property 'class' stores known labels.
points = ee.FeatureCollection('GOOGLE/EE/DEMOS/demo_landcover_labels')

# This property of the table stores the land cover labels.
label = 'landcover'

# Overlay the points on the imagery to get training.
training = image.select(bands).sampleRegions(**{
    'collection': points,
    'properties': [label],
    'scale': 30
})

# Define visualization parameters in an object literal.
vizParams = {'bands': ['B5', 'B4', 'B3'],
             'min': 0, 'max': 1, 'gamma': 1.3}


Map.centerObject(points, 10)
Map.addLayer(image, vizParams, 'Image')
Map.addLayer(points, {'color': "yellow"}, 'Training points')

first = training.first()
print(first.getInfo())


# %%
"""
## Display Earth Engine data layers 
"""

# %%
Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.
Map